Statistical Physics-Based Model of Solid Electrolyte Interphase Growth in Lithium Ion Batteries
Bibliographic record
Abstract
The article presents a statistical physics-based model for the growth of the solid electrolyte interphase (SEI) in the negative electrode of lithium ion batteries.During battery operation, the SEI thickness grows by the reaction between lithium ions, electrons and solvent species on the surface of active particles at the negative electrode.The growth of the SEI layer causes a loss of lithium ions that induces capacity fade.In addition, it increases the ion transport resistance and decreases the total porosity.Our model employs a population balance formalism based on the Fokker-Planck Equation to describe the propagation of the particle density distribution function in the electrode.Structure-transforming processes at the level of individual particles are accounted for by using a set of kinetic and transport equations.These processes alter the particle density distribution function, and cause changes in battery performance.A parametric study of the model singles out the first moment of the initial SEI thickness distribution as the determining factor in predicting the capacity fade.The model-based treatment of experimental data allows analyzing processes that control SEI growth and extracting their controlling parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".